Conventionally, as a method for obtaining an image feature amount, the Kanade-Lucas-Tomasi (KLT) method is known. In the KLT method, a feature amount Ri,j is obtained by the formula below. In the formula, i and j are coordinate values in a vertical direction and a horizontal direction respectively, and both are integers.
                    [                  Formula          ⁢                                          ⁢          1                ]                                                                      R                      i            ,            j                          =                                            X                              i                ,                j                                      +                          Y                              i                ,                j                                      -                                                                                (                                                                  X                                                  i                          ,                          j                                                                    -                                              Y                                                  i                          ,                          j                                                                                      )                                    2                                +                                  4                  ·                                      XY                                          i                      ,                      j                                        2                                                                                2                                    (        1        )            
In the above formula (1), Xi,j and Yi,j are respectively a sum of squares of horizontal direction gradients x and a sum of squares of vertical direction gradients y of pixel values of pixel coordinates (i,j). XYi,j is a sum of products of horizontal direction gradients x and vertical direction gradients y of pixel values of pixel coordinates (i,j). When a target image area is 7 pixels by 7 pixels, the horizontal direction gradient x and the vertical direction gradient y are calculated after a target pixel is filtered by a 7 by 1 filter and a 1 by 7 filter to reduce noise effect.
Xi,j, Yi,j, and XYi,j are represented by the formula (2), the formula (3), and the formula (4) below respectively.
                    [                  Formula          ⁢                                          ⁢          2                ]                                                                      X                      i            ,            j                          =                              ∑                          a              =                              i                -                3                                                    i              +              3                                ⁢                                    ∑                              b                =                                  j                  -                  3                                                            j                +                3                                      ⁢                                          x                                  a                  ,                  b                                            ×                              x                                  a                  ,                  b                                                                                        (        2        )                                [                  Formula          ⁢                                          ⁢          3                ]                                                                      Y                      i            ,            j                          =                              ∑                          a              =                              i                -                3                                                    i              +              3                                ⁢                                    ∑                              b                =                                  j                  -                  3                                                            j                +                3                                      ⁢                                          y                                  a                  ,                  b                                            ×                              y                                  a                  ,                  b                                                                                        (        3        )                                [                  Formula          ⁢                                          ⁢          4                ]                                                                      XY                      i            ,            j                          =                              ∑                          a              =                              i                -                3                                                    i              +              3                                ⁢                                    ∑                              b                =                                  j                  -                  3                                                            j                +                3                                      ⁢                                          x                                  a                  ,                  b                                            ×                              y                                  a                  ,                  b                                                                                        (        4        )            
In accordance with the formula (1), when Xi,j or Yi,j is 0, the feature amount Ri,j is 0. Since the formula (5) is true when the horizontal direction gradient x and the vertical direction gradient y are the similar in accordance with the formulas (2) to (4), the feature amount Ri,j is 0 in accordance with the formula (1). In other words, in the KLT method, a point in which there are both a horizontal direction gradient and a vertical direction gradient and the sizes of the horizontal direction gradient and the vertical direction gradient are different is defined as a feature point. Non Patent Documents 1 and 2 discuss the KLT method for obtaining an image feature amount as above.[Formula 5]Xi,j×Yi,j=XYi,j×XYi,j  (5)    Non Patent Document 1: Bruce D. Lucas, et al. “An Iterative Image Registration Technique with an Application to Stereo Vision”, Proc 7th Intl Joint Conf on Artificial Intelligence (IJCAI), Aug. 24 to 28, 1981, Vancouver, British Columbia, p. 674-679.    Non Patent Document 2: Carlo Tomasi, et al. “Detection and Tracking of Point Features”, Technical Report CMU-CS-91-132, April 1991, p. 1-20.